Diffusers中基于Stable Diffusion的哪些图像操作

目录

• 辅助函数
• Text-To-Image
• Image-To-Image
• In-painting
• Upscale
• Instruct-Pix2Pix

基于Stable Diffusion的哪些图像操作们：

• Text-To-Image generation：StableDiffusionPipeline
• Image-to-Image text guided generation：StableDiffusionImg2ImgPipeline
• In-painting: StableDiffusionInpaintPipeline
• text-guided image super-resolution: StableDiffusionUpscalePipeline
• generate variations from an input image：StableDiffusionImageVariationPipeline
• image editing by following text instructions：StableDiffusionInstructPix2PixPipeline
• ......

辅助函数

import requests
from PIL import Image
from io import BytesIO

def show_images(imgs, rows=1, cols=3):
    assert len(imgs) == rows*cols
    w_ori, h_ori = imgs[0].size
    for img in imgs:
        w_new, h_new = img.size
        if w_new != w_ori or h_new != h_ori:
            w_ori = max(w_ori, w_new)
            h_ori = max(h_ori, h_new)
    
    grid = Image.new('RGB', size=(cols*w_ori, rows*h_ori))
    grid_w, grid_h = grid.size
    
    for i, img in enumerate(imgs):
        grid.paste(img, box=(i%cols*w_ori, i//cols*h_ori))
    return grid

def download_image(url):
    response = requests.get(url)
    return Image.open(BytesIO(response.content)).convert("RGB")

Text-To-Image

根据文本生成图像，在diffusers使用StableDiffusionPipeline实现，必要输入为prompt，示例代码：

from diffusers import StableDiffusionPipeline

image_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")

device = "cuda"
image_pipe.to(device)

prompt = ["a photograph of an astronaut riding a horse"] * 3
out_images = image_pipe(prompt).images
for i, out_image in enumerate(out_images):
    out_image.save("astronaut_rides_horse" + str(i) + ".png")

示例输出：

Image-To-Image

根据文本prompt和原始图像，生成新的图像。在diffusers中使用StableDiffusionImg2ImgPipeline类实现，可以看到，pipeline的必要输入有两个：prompt和init_image。示例代码：

import torch
from diffusers import StableDiffusionImg2ImgPipeline

device = "cuda"
model_id_or_path = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
pipe = pipe.to(device)

url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
init_image = download_image(url)
init_image = init_image.resize((768, 512))

prompt = "A fantasy landscape, trending on artstation"

images = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images

grid_img = show_images([init_image, images[0]], 1, 2)
grid_img.save("fantasy_landscape.png")

示例输出：

In-painting

给定一个mask图像和一句提示，可编辑给定图像的特定部分。使用StableDiffusionInpaintPipeline来实现，输入包含三部分：原始图像，mask图像和一个prompt，

示例代码：

from diffusers import StableDiffusionInpaintPipeline

img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"

init_image = download_image(img_url).resize((512, 512))
mask_image = download_image(mask_url).resize((512, 512))

pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16)
pipe = pipe.to("cuda")

prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
images = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images
grid_img = show_images([init_image, mask_image, images[0]], 1, 3)
grid_img.save("overture-creations.png")

示例输出：

Upscale

对低分辨率图像进行超分辨率，使用StableDiffusionUpscalePipeline来实现，必要输入为prompt和低分辨率图像(low-resolution image)，示例代码：

from diffusers import StableDiffusionUpscalePipeline

# load model and scheduler
model_id = "stabilityai/stable-diffusion-x4-upscaler"
pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16, cache_dir="./models/")
pipeline = pipeline.to("cuda")

# let's download an  image
url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale/low_res_cat.png"
low_res_img = download_image(url)
low_res_img = low_res_img.resize((128, 128))

prompt = "a white cat"
upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]
grid_img = show_images([low_res_img, upscaled_image], 1, 2)
grid_img.save("a_white_cat.png")
print("low_res_img size: ", low_res_img.size)
print("upscaled_image size: ", upscaled_image.size)

示例输出，默认将一个128 x 128的小猫图像超分为一个512 x 512的：

默认是将原始尺寸的长和宽均放大四倍，即：

input: 128 x 128 ==> output: 512 x 512
input: 64 x 256 ==> output: 256 x 1024
...

个人感觉，prompt没有起什么作用，随便写吧。

关于此模型的详情，参考。

Instruct-Pix2Pix

重要参考

根据输入的指令prompt对图像进行编辑，使用StableDiffusionInstructPix2PixPipeline来实现，必要输入包括prompt和image，示例代码如下：

import torch
from diffusers import StableDiffusionInstructPix2PixPipeline

model_id = "timbrooks/instruct-pix2pix"
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, cache_dir="./models/")
pipe = pipe.to("cuda")

url = "https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png"
image = download_image(url)

prompt = "make the mountains snowy"
images = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=1.5, guidance_scale=7).images
grid_img = show_images([image, images[0]], 1, 2)
grid_img.save("snowy_mountains.png")

示例输出：